Low complexity decoding algorithm for tail-biting convolutional codes

ABSTRACT

A method for decoding tail-biting convolutional codes. The method includes initializing a correction depth, selecting a first starting state from a set of encoding states, and initializing a metric value for the selected starting state as zero and the other states as infinity. The input bit stream is read and a Search Depth Viterbi algorithm (SDVA) is performed to determine path metrics and identify a minimum-metric path. The ending state for the minimum-metric path is determined and the output for this ending state is identified as “previous output.” A second starting state is set to the ending state of the minimum-metric path, and symbols equal to the correction depth from the previous output are read. The SDVA is performed on the second set of read symbols to generate a corrected output. A decoded output is generated by replacing symbols at the beginning of the previous output with the corrected output.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.12/945,661 filed on Nov. 12, 2010 which is a continuation of U.S. patentapplication Ser. No. 11/687,543 filed on Mar. 16, 2007 which claims thepriority benefit of Chinese application Serial No. 200610084044.2 filedMar. 31, 2006. All of which are incorporated herein in their entirety bythis reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates, in general, to error correction incommunication systems such as errors produced in channels in wireless ordigital radio communications between devices, and, more particularly, tosoftware, systems, and methods for decoding tail-biting convolutionalcodes at a receiver, such as an uplink receiver for an orthogonalfrequency division multiplexing access (OFDMA) communication system ornetwork.

2. Relevant Background

There are ongoing efforts to provide next generation mobilecommunications and to improve existing communications among networkeddevices including among wireless devices. A recent development has beento not only do away with wires or cables within a building or home butalso to do away with the cables coming into the building or home. WiMAXtechnology (also known as 802.16 because it is based on the IEEE 802.16WirelessMAN Standard for Wireless Metropolitan Area Networks) promisesto allow this jump to more wireless communications that function as awireless alternative to cable modems and DSL (Digital Subscriber Line).WiMAX will likely offer connectivity at up to 30 miles from an antennaat speeds up to 75 mbps (megabytes per second) and at higher rates orspeeds under 5 miles, whereas a cable modem may only offer speeds of 1mbps. As a result, where cable and telephone companies do not offerbroadband Internet connections, WiMAX technology offers a way to providebroadband Internet, digital TV, and other digital communications withthe use of a wireless antenna to pick up a WiMAX signal that is thendistributed wireless (or with wires) throughout the local area by a basestation (BS) to user terminals or devices (e.g., to subscriber stations(SSs)). The OFDM (Orthogonal Frequency Division Multiplexing) techniquehas been widely proposed in WiMAX and many other wireless systems toprovide high data rate transmission. OFDM uses a set of overlapping butorthogonal sub-carriers to realize high spectrum efficiency. Morerecently, combined with TDMA (Time Division Multiple Access) and/or FDMA(Frequency Division Multiple Access), OFDMA has been proposed in manybroadband wireless systems such as WiMAX systems.

An ongoing challenge in any communication system, including WiMAXsystems such as those defined by IEEE 802.16d/e, is how to correcterrors in the signal between a transmitting device and a receivingdevice. Information signals or data transmitted between a transmitterand a receiver via a communication channel can be corrupted, i.e.,errors can be introduced into the signal or data before it is receivedat the receiver. A communication channel is any medium (wired orwireless) between a transmitter and a receiver. The medium may introducenoise, interference, or other channel errors, and any medium that mayinject errors can be thought of as a channel. The medium may be astorage medium or a communication or data transfer medium. In apractical radio communication system, noise is also picked up by theantenna or is generated within the front end amplifiers of the receiver.So, effective methods must be taken to correct errors generated byadding all these kinds of noise on a received signal.

Error detection and error correction codes are widely employed indigital communication systems to ensure the transmission reliability,and such codes are typically provided by channel encoding and decoding,i.e., an encoder in the transmitting device and a decoder in thereceiving device. The use of such error correction acts to assist inprotecting against channel errors, which occur in situations such aswhere there is noise, fading, multipath and interference such as whensignal transmission or reception is in an elevator, in a moving car,amidst tall buildings, in hilly terrain, or the like. By addingredundancy or redundant bits into the transmitted information bits,error control techniques enable receivers to detect or even correct theerrors introduced by channel distortion. While it is useful to addredundant bits to facilitate error correction, transmission of theredundant bits also consumes additional bandwidth and their numbershould be limited where possible to limit bandwidth usage.

One well-known technique for error-correction is by encoding thetransmitted signal or data using convolutional codes (CC). Convolutionalcodes are generally described by a coding rate, a constraint length, andgenerator polynomials. In a convolutional encoder, each input bittravels through each memory element such that the number of output bitsgenerated by the encoder corresponds to the number of input bits and thecoding rate of the convolutional encoder. One method of convolutionalencoding is to use “tail bits” to flush the encoder by initializing eachmemory element with a zero (e.g., to force a reset to the zero state)and the tail bits are added at the end of the input frame or stream astails, which caused the increase of frame or stream length. FIG. 1illustrates an example of a tail bits convolutional codes encoder 100that receives an input stream of bits 102, has two memory elements 104,106, two adder circuits 108, 110, and produces two output bits 120. Theencoder 100 has a coding rate of ½ and a constraint length of 3, and asshown at this rate, one bit input 102 produces two bits output 120. Itshould be pointed out that the actual output bit 120 is not only relatedto the actual input 102 but also is related to the previous inputs frommemory of the encoder or transmitter embodying the encoder 100. As atail bits convolutional codes encoder 100, its shift registers shall beinitialized by a definitive code (in most cases, an all-zero code), andthis code is appended to the end of the information block to be encoded,which ensures that the encoder has the same starting state and endingstate.

A well-known decoding technique performed at a receiver forconvolutional codes is the Viterbi algorithm (VA) based on a trellis.FIG. 2 shows a trellis structure 200 of tail bits convolutional codes,i.e., for the encoder structure 100 in FIG. 1. The two shift registers104, 106 of the encoder 100 will generate four different states at onestage in the trellis 200. The nodes in each column stand for thedifferent states at each stage. The solid lines represent the statetransition if the input bit is 0; while the dotted lines represent thestate transition if the input bit is 1. The two values in theparentheses above these lines are the corresponding outputs. Thefundamental idea of the VA is to obtain the minimum distance (or pathmetric) path from a starting state to an ending state. The distance iscalculated between the decoding input and the output in thecorresponding location of the trellis and stored in decoder memory (orreceiver memory) as a branch metric. An Add-Compare-Select (ACS) processis taken to select a survival path. The branch metric accumulated alonga survival path is denoted as a path metric. For each state at onestage, there will be only one survival path and one relevant path metricvalue. For instance for the encoder shown in FIG. 1, since both thestarting state and ending state are “00”, the survival path must beginand end at the state “00”. At the end of the last stage, the VA startsfrom the state “00” and traces back to the state “00” at the first stageto retrieve the final decoding outputs.

An ACS process 300 for the VA is detailed in FIG. 3. The value above thestate is designated for the path metric; while the value above thetransition branch is for the branch metric. Two branches merge into onestate in the ACS process 300. On the assumption that the VA isdetermining the survival path on the state “00” at the k-th stage, itneeds to compare the accumulated path metrics of the “up” branch and the“down” branch. For example, the up branch merging into the state “00” atthe k-th stage derives from the state “00” of the (k-1)-th stage. Thepath metric value on the state “00” of the (k-1)-th stage is 0.22, andthe branch metric of the transition between these two states is 0.11.Thus, the up path metric on the state “00” at the k-th stage isaccumulated to 0.33 (i.e., 0.22+0.11). In the same way, the down pathmetric on the state “00” at the k-th stage is accumulated to 0.22 (i.e.,0.16+0.06). After comparing the up and down path metrics, the VA decidesto take the down path, i.e. the smaller one, as the survival path andsaves the value 0.22 as the path metric on the state “00” at the k-thstage. The same operations are taken on the other states at the k-thstage.

As mentioned above, the tail bits convolutional codes encoder has apriori knowledge of starting and ending state in trellis diagram, whichmakes the VA useful for easily and reliably decoding tail bitsconvolutional codes. However, the appended tails reduce the bit rate tosome extent since the tail bits must also be passed through the encoder.In order to maintain a higher bit rate, tail-biting convolutional codeshave been proposed for use in encoding to provide forward errorcorrection in communication systems.

FIG. 4 illustrates an exemplary encoder 400 employing tail-bitingconvolutional codes with a coding rate of ½ and a constraint length of3. The encoder 400 receives an input bit stream 402, includes a pair ofmemory elements or shift registers 404, 406 and a pair of adder circuits408, 410, and outputs to output bits 420. Different from the previoustail bits CC encoder 100, its shift registers 404, 406 are initializedby the last 2 bits of the information 402 to be encoded. The encoder 400still guarantees that the starting and ending states are the same in itstrellis, but the decoder 400 has no knowledge of the starting and endingstates before decoding, which makes the decoding process much morecomplex than that of signal or data stream received from a tail bitsconvolutional codes encoder.

FIG. 5 illustrates the trellis diagram structure 500 of tail-bitingconvolutional codes when the VA is used for decoding. The decoder oftail-biting convolutional codes is only aware that the starting stateand the ending state will be the same. One possible decoding method isto try all the states as a tentative starting state on one-by-one basis.For each starting state, the decoder would find the path with minimumpath metric at the last stage as a tentative survival path. After tryingall the starting states, the tentative survival path with the samestarting state and ending state would be regarded as the final survivalpath. However, such a decoding algorithm or technique would need toperform the VA from the first stage to the last stage for 2^(km) time(where “m” stands for number of input bit in one instant and “k” for theshift register size). As a result, such a decoding method is far toocomplex to satisfy a real-time system software implementation, such asin a digital signal processor (DSP) of a communication system receiver.

The communication industry continues, therefore, to demand alow-complexity tail-biting CC decoding algorithm with a sound errorprotection performance. This search for an improved decoding method, anddecoders embodying such a decoding method, has more recently become evenmore important. This is because tail-biting convolutional codes have theadvantage of saving bit rate without adding special tail bits to theencoded block. As a result, these encoding techniques have been adoptedas standards for certain types of communication systems. For example, ithas been used to encode the short message in North American DigitalCellular Radio (IS-54). Additionally, WiMAX systems are expected tobecome increasingly important in the wireless communication industry,and tail-biting convolutional codes have been adopted as a mandatoryforward error correction scheme in the OFDMA mode of IEEE 802.16d/esystems.

Existing decoders have not satisfied all of the demands of thecommunication industry, e.g., the demands for lower computationalcomplexity and for reduced memory usage by the decoder. For example, oneproposed decoding method [R. V. Cox and C-E. W. Sundberg, “An EfficientAdaptive Circular Viterbi Algorithm for Decoding Generalized Tail-bitingConvolutional Codes”, IEEE Transactions on Vehicular Technology, Vol.43, No. 1, pp. 57-68, February 1994] includes starting from all thestates, saving the corresponding best-trellis-path, and decoding ametric score each time. The method then continues with comparing all thesaved decoding-metric-scores to select the best one and then, giving outits corresponding output bit stream as the correct and most likelydecoding result. However, such a decoding method is too complex forimplementation in real-time communication applications and systems as itinvolves the standard Viterbi algorithm being performed 2^(mk) times.

Two other proposed decoding schemes, i.e., the “Bar-David” scheme andthe “two-steps scheme,” also fail to address the concerns of thecommunications industry [H. H. Ma and J. K. Wolf, “On Tail BitingConvolutional Codes”, IEEE Transactions on Communications, Vol.COM-34,No. 2, pp. 104-111, February 1986; Q. Wang and V. K. Bhargava, “AnEfficient Maximum Likelihood Decoding Algorithm for Generalized TailBiting Convolutional Codes Including Quasicyclic Codes”, IEEETransactions on Communications., Vol. 37, No. 8, pp. 875-879, August1989]. In the Bar-David decoding method, the processing steps include:(1) choosing an arbitrary starting state; (2) decoding using a maximumlikelihood decoder which finds the best path from that starting state toall possible ending states; (3) checking the decoded code word to see ifthe starting state is the same as the ending state and if yes, stopping,otherwise going a next step; (4) using the previous ending state as thenew starting state, checking to see if this starting state has beentried before, and if yes, going to step 1, otherwise returning to step2. The Bar-David method has a number of disadvantages. The standardViterbi algorithm needs to be performed at least twice, except that thefirst chosen starting state is just the real starting state. Innon-optimum occasions, the Bar-David computational complexity is thesame as that in other proposed methods (i.e., repeating the VA algorithm2^(mk) times), and so, this method is still too complex forimplementation and real-time application. In the proposed two-stepmethod, the processing steps include: (1) obtaining an ordered list ofthe 2^(mk) starting states using an algebraic method called “continuedfractions” and (2) performing the VA using each entry on the orderedlist as their starting state. Again, this method requires the VA to berun many times and as a result, it is too complex for most practicalcommunications applications.

Several decoding schemes for tail-biting convolutional codes have beensuggested in issued patents, but none have been widely adopted byindustry, which is likely due to their significant computationcomplexities and memory usage. For example, U.S. Pat. No. 6,256,764 toAtallah describes a process involving: (1) giving the same possibilityto all the starting states; (2) performing the Viterbi algorithm once tofind an ending state with lowest ending score and making a listincluding all the ending states within a certain value range to thelowest ending score; (3) performing the Viterbi algorithm with everylist state as a starting state and recording the corresponding score;and (4) comparing all the recorded scores of the last step, selectingthe lowest one, and saving it as the real ending and starting state.Again, this method requires that the Viterbi algorithm be run manytimes, which increases its computational complexity.

U.S. Pat. No. 6,877,132 to De et al. describes a decoding method thatincludes the steps of: (1) modifying the input bit stream by repeatingpart of the input decoding bits; (2) assuming a probability for eachpossible initial state of the encoder; (3) decoding each symbol of theinput decoding bit stream using majority logic with reference to atrellis structure corresponding to the encoder; and (4) reorganizing theoutput bit stream according to the rules of used for modifying the inputbit stream. This method has a number of disadvantages including the bitstream needing to be modified both before and after decoding and theViterbi algorithm needing to be performed many times with relativelyhigh complexity. As a result, this method does not provide a usefulgeneral decoding algorithm for tail-biting convolutional codes.

U.S. Pat. No. 5,349,589 to Chennakeshu et al. describes another decodingmethod, but as with the other methods discussed above, this method hasnot been widely adopted by the communications industry. The decodingmethod includes: (1) separating an encoded frame of data into key bits,critical bits, and unprotected bits in which the key bits and criticalbits in the received stream are encoded with tail-biting convolutionalcodes and are merged with unprotected bits; (2) splitting the receiveddata into convolutionally encoded bits and unprotected accordingly; and(3) starting from all the possible states, decoding the convolutionallyencoded bits into a number of possible paths using a generalized Viterbialgorithm and then finding the path which provides a lowest path metric.This method is not entirely acceptable for implementation instandards-based encoders and decoders because both the encoding anddecoding data are special. Hence, this method does not provide a generaldecoding algorithm useful for tail-biting convolutional codes.

From this discussion, it can be seen that these suggested decodingmethods typically perform the VA repeatedly until an end state matches astarting state, with the main difference between the methods being theway the search for the real starting state is performed. The number ofVA iterations is generally so high that these methods are undesirablycomplex for implementation in a real-time software application, such aswould be used in a digital signal processor (DSP) of a communicationreceiver. In bad channel conditions (such as those with a low signal tonoise ratio), the trellis or decoding path that has the same startingstate and ending state may not even exist, which causes theaforementioned methods to simply not work.

Hence, there remains a need for an improved method, and associateddecoders, for decoding data that has been encoded using tail-bitingconvolutional codes. Preferably, such a decoding method would bedesigned with lower computational complexity and memory usage whencompared with prior decoding schemes, and further, it is preferable thatsuch a decoding method would be well-suited for use in standardcommunication systems and networks, such as in WiMAX and other wirelessreceiving devices (e.g., an OFDMA mode device and the like) includingthose configured according to the IEEE 802.16 standard.

SUMMARY OF THE INVENTION

The present invention addresses the above problems by providing achannel decoding algorithm or method (and decoders configured to runsuch a decoding method) for decoding tail-biting convolutional codes.The decoding method has relatively low complexity and is useful fordecoding in nearly any communication system such as a digital radiocommunication system and is particularly well suited for decodersprovided in OFDMA uplink receivers and other WiMAX receivers. Asdiscussed above, employing a maximum likelihood decoder, known asViterbi decoder, for tail-biting convolutional codes will cause someproblems due to the fact that the decoder is unaware of the encoder'sstarting state. Most prior encoding and decoding error correctionmethods use tail-biting over short encoder blocks. Unfortunately, inorder to find the encoder starting state, a Viterbi decoder has to berun several times, which makes implementation in an operationcommunication system difficult. In standard IEEE 802.16d/e system OFDMAmode, tail-biting is adopted as the mandatory channel coding scheme, andas a result, the encoder blocks are not short.

With this in mind, the present invention provides an efficient decodingtechnique for tail-biting convolutional codes with desired errorprotection results but with less computational consumption that isparticularly well-suited for use with IEEE 802.16d/e and similarcommunication devices. Briefly, the decoding method of the inventionuses a search depth Viterbi decoding method, and only runs a Viterbialgorithm one or two times (i.e., more than once but at most twice) overthe received encoded block or input stream. Thus, the decoding methodgreatly reduces the computational and implementation complexity thatwill facilitate adoption of WiMAX and other receivers with a decoderproviding this decoding method such as in a low-complexity tail-bitingconvolutional codes decoder suitable for the requirements of mobileradio communication systems like those built to IEEE 802.16d/e.

More particularly, a method is provided for decoding a bit stream orsignal that is transmitted over a channel after being encoded by atail-biting convolutional codes encoder. The method includesinitializing a correction depth that defines a number of bits or symbolsin the first portion of a received stream or block that will beprocessed twice for error correction and replaced (i.e., with correct orcorrected code). The correction depth or length is an experientiallydetermined value that is selected based on the length of the receivedblock and the channel through which it was transmitted (e.g., from atransmitter to a receiver performing the decoding method). The methodcontinues with selecting a first starting state from a set of encodingstates and initializing a metric value for the selected starting stateas zero and the other states as infinity. Then, a number equal to theblock length of the symbols from the input bit stream are read and aViterbi algorithm is performed on these symbols from the first startingstate to determine path metrics and to identify a minimum-metric pathbased on the determined path metrics. The ending state for theminimum-metric path is determined and the output associated with thisending state is identified as the “previous output.” The method thencontinues with setting a second starting state equal to the ending stateof the minimum-metric path, and second reading a number of the symbolsfrom the previous output, but the number of symbols read are equal tothe correction depth. Then the Viterbi algorithm is performed on thesecond set of read symbols from the second starting state to generate acorrect or corrected output. A decoded final output is generated byreplacing a number of the symbols equal to the correction depth in theprevious output with the correct or corrected output.

To save memory and to decrease the complexity of the decoding process,the Viterbi algorithm may be implemented as a “Search Depth” Viterbialgorithm (SDVA), and the method includes initiating a search depthvalue (SD) based on the block length (L) and the constrain length (e.g.,SD is generally defined as 5 to 7 times or other multiples of theconstraint length). In performing the SDVA, the path metrics arecalculated for a number of stages equal to the search depth, with thecalculating beginning from the starting state and terminating at allpossible ending states. Based on this calculating process, the pathhaving the minimum path metric at a stage of SD-1 is identified. TheSDVA also includes calculating from a first stage to a stage of L-2 andperforming this calculating for a number of iterations equal to L-SD-1.Then, the SDVA continues with providing outputs of the last SD bitsaccording to the minimum path metric. The decoding method is generallysuited to decoding tail-biting convolutional codes including thosedefined by standards such as IEEE 802.16d/e.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an encoder structure for encoding a bit stream usingtail bits convolutional codes;

FIG. 2 illustrates a trellis diagram for tail bits convolutional codes;

FIG. 3 illustrates an Add-Compare-Select (ACS) process used in a Viterbialgorithm to decode data streams encoded using convolutional codes;

FIG. 4 shows an encoder structure for encoding a bit stream usingtail-biting convolutional codes;

FIG. 5 illustrates a trellis diagram for tail-biting convolutionalcodes;

FIG. 6 is a simplified block diagram of a communication system ornetwork according to an embodiment of the invention;

FIG. 7 is a simplified process flow or structure for a proposed decodingmethod of the present invention;

FIG. 8 illustrates a Search Depth Viterbi Algorithm (SDVA) processuseful for decoding according to the invention;

FIG. 9 is a flow chart of a representative decoding method of theinvention, such as may be provided by the decoders of FIGS. 6 and 14 anddecoding module in FIG. 11;

FIG. 10 illustrates an encoder structure to provide tail-bitingconvolutional codes encoding;

FIG. 11 illustrates a test system used for simulating encoding anddecoding in a communication system having a channel that potentiallyintroduces errors in data transmissions;

FIG. 12 is a graph of a BER performance comparison based on operation ofthe system of FIG. 11 of tail bits and tail-biting convolutional codesunder SUI-4 Channel in which the decoding method of the invention wasused for decoding data streams that were encoded using the tail-bitingconvolution codes;

FIG. 13 is a graph of a throughput comparison of tail bits andtail-biting convolutional codes under SUI-4 Channel during operation ofthe simulation system of FIG. 11; and

FIG. 14 is a block diagram of a radio or wireless signal receivingsystem in which embodiments of the decoding method of the presentinvention, such as depicted in FIG. 9, may be implemented.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention is directed to methods (and components such asdecoders implementing such methods and receivers or receiving deviceswith such decoders) that are designed to provide effective decoding ofsignals or data streams encoded using tail-biting convolutional codesbut yet to require less computational-complexity and memory usage.

FIG. 6 illustrates a communication system 610 in which the tail-bitingconvolutional codes decoding techniques of the present invention may beutilized. As shown, the system 610 includes a base station or BS 620that is in communication with one or more public or private networks,e.g., Internet 640 and communication network 646, and in typicalembodiments, the link to networks 640, 646 is a wired or wirelesscommunication link, such as those defined between BSs and networks inIEEE 802.16 or the like (e.g., a WiMAX network-BS link). The BS 620 isalso in communication via user or SS signals 638 with SSs 630. Thesignals 638 are encoded for forward error correction and may bewireless, random access signals or the like as defined by standards suchas the WiMAX standard IEEE 802.16. The BS 620 includes an uplinkreceiver 624 and memory 628 which is accessible by the receiver 624. Theuplink receiver 624 functions to decode signals 638 from the SS 630 andto this end, includes a channel decoder 626 that uses a search depthViterbi algorithm (SDVA) module 627, which may be provided as softwareand/or hardware components, to provide low-complexity decoding of thebit stream 638 as is discussed in great detail below. The SS includes atransmitter 634 that uses a tail-biting CC encoder 635 (which may be asoftware module and/or hardware components) to encode the signals 638including using SS memory 636 (e.g., shift registers or the like usefulfor encoding as discussed earlier). The system 610 is simplified in partbecause typical communication devices will implement both a transmitterand a receiver for encoding and decoding the signals or data streams 638(e.g., the BS 620 would also include a transmitter similar totransmitter 634 and the SS 630 would include a receiver with a channeldecoder similar to decoder 626). Hence, the particular configuration ofthe system 610 is not limiting to the invention, with the system 610representing in general a representative WiMAX communication system,such as may be established to provide broadband Internet 640 or networkaccess or digital TV access 646 in a wired or wireless manner for aplurality of users and/or SSs 630 via a base station 620.

To practice the invention, the computer, network devices, andcommunication devices, such as the user/client devices or subscriberstations (SSs) and base stations (BSs), may be any devices useful forproviding the described functions, including well-known data processingand communication devices and systems such as wireless and cellularphones and similar devices; computer systems; personal digitalassistants; personal, laptop, and notebook computers; mobile computingand/or communication devices with processing, memory, and input/outputcomponents; and server devices configured to maintain and then transmitdigital data. Data typically is communicated in digital format followingstandard wired and wireless communication and transfer protocols, suchas TCP/IP, HTTP, and the like, or IP or non-IP wireless communicationprotocols such as TCP/IP, TL/PDC-P, WSP, Bluetooth, IEEE 802.11b, 802.16(WiMAX), and/or other IEEE standards, and/or other protocols or formatsyet to be developed or evolved, but this is not intended as a limitationof the invention. Typical embodiments are configured to support forwarderror correction in communication systems implementing the OFDMA mode ofIEEE 802.16 and its encoding procedures but the teachings of theinvention may be implemented in other communication systems such asradio communication systems utilizing short block signals.

The following discussion provides a detailed explanation of the SDVAdecoding method of the invention and how it may be beneficiallyimplemented within communication systems. Compared with the traditionaltail bits convolutional codes, the shift registers of tail-bitingencoders, such as encoder 635, are initialized by the last data bits ofthe data block being encoded. Therefore, the starting state and endingstate of a trellis diagram for a tail-biting decoder, such as decoder626, are data-dependent and unknown before the decoding procedure isbegun, and additionally, the starting state will equal to ending state.To obtain enhanced performances of a tail-biting CC decoder, theinventors initially experimented with a complex decoding algorithm basedon a conventional VA. In such a decoding method or algorithm, one stateis randomly chosen as a starting state, a VA is performed to accumulatethe paths metrics to all possible ending states, and the minimum-metricpath is identified. This algorithm is iterated until all possiblestarting states are taken. After many simulations, some interestingphenomena were found by the inventors to occur. Specifically, when adecoded block size is bigger than 100 bits and E_(b)/N_(o) (i.e., thesignal-to-noise-ratio) is over a certain threshold value (e.g.,approximately 1 to 3 decibels according to different channelsconditions), the paths from all tentative starting states tend toterminate on one specific state. If this specific ending state derivedfrom the last VA iteration is set as the starting state for the next VAiteration, the decision errors between the two successive iterations aredifferent only in some of the beginning bits. Based on this and otherinformation and conclusions, the inventors determined their decodingmethod.

The proposed decoding method performs a VA one to two times (i.e., morethan once but no more than twice). A state is randomly chosen as atentative starting state, and the VA is performed from this tentativestarting state to accumulate path metrics. The minimum-metric path andits corresponding ending state are identified, and then, the outputaccording to the ending state is called a previous output. This endingstate is taken as the tentative starting state for the next VAiteration. The VA is performed again from this specified starting statebut only over some symbols (i.e., a subset of the bit stream beingdecoded) to give a correct or corrected output. Then, the correct outputis used to replace the corresponding previous output bits to get thefinal output. An embodiment of a decoding process 700 of the inventionis shown generally as described in FIG. 7 in which the previous output710 is processed firstly to get the previous output and the startingstate for correct output 720. With the starting state and thepre-identified correction depth, correct output is processed to get thecorrect output. At last, the final output 730 is found by replacing thecorresponding place in previous output with the correct output. Clearly,the stage at which the second VA iteration stops is an importantparameter of the decoding process 700. This iteration stop location isdenoted as “Correction Depth” in FIG. 7 or CD. The CD is typically anexperiential value that is selected depending on decoded block size ofthe input bit stream (or previous output) and also on the channelenvironment.

Since a conventional VA has to trace back from the ending state to thestarting state along the trellis to give out the outputs, it isdifficult to produce some partial outputs needed for correct output.Moreover, a larger decoded block size results in an increasing of memoryusage. To address these problems, the new algorithm or decoding methodof the invention preferably uses a “Search Depth Viterbi Algorithm” orSDVA instead of a conventional VA. The Search Depth Viterbi Algorithm isanother applying style of a VA but that is different from the generaltraceback style. Major advantages of the SDVA are that its memory usagehas no relation with decoding block size, and the SDVA gives out theoutputs as part of the decoding procedure.

A simple SDVA diagram 800 is shown in FIG. 8. As shown, the SDVA process800 can be divided into 3 steps shown at 810, 820, and 830. The firststep 810 is an initial step during which all the path metrics of thefirst search depth (SD) stages are calculated. The calculations beginfrom the selected beginning state and terminate at all the possibleending states. Then, the path with the minimum path metric at the stageSD-1 is selected, and the corresponding first output bit 0 is given. Thesecond step 820 is the calculation step. This step involves calculatingfrom the 1st stage to (L-2)th stage and includes (L-SD-1) iterations,where “L” is the block size or length of the input bit stream. The firstiteration will be presented as an example. After finishing the firststep 810, all the path metrics at the stage 0 are discarded and those ofstage SD are added. The new survival path is selected, and the outputbit 1 is given according to the path metric on stage SD. The sameprocess is employed to the other iterations in this step 820. The thirdstep 830 of the SDVA process 800 is the final output step. In this step830, the last iteration is calculated according to the minimum pathmetric to give the outputs of the last SD bits. In contrast to a generalVA, the SDVA 800 can stop at any iteration so as to give a part orsubset of the output bits.

FIG. 9 illustrates an SDVA decoding method 900 that is useful fordecoding bit streams that were encoded using a tail-biting convolutioncodes encoder. This method may be provided by running a decoder ordecoding module configured according to the present invention. Themethod 900 starts at 910 such as by providing an SDVA decoding module ina receiver DSP or the like. At 920, the method 900 includes initializingthe search depth (SD) and the correction depth (CD) values according tothe block size L and the transmit channel environment in which thetransmitter and receiver (or other device running a decoder function toprovide the method 900) are operating. Step 920 also includes settingthe initial metric value of the selected initial starting state to 0 andall the others to infinity. At 926, the method 900 continues withreading the intended L input decoding symbols (i.e., the received,encoded input bit stream from a transmitter running a tail-biting CCencoder).

The decoding method 900 continues at 930 with running the Search DepthViterbi Algorithm SDVA) for the first time on the L read symbols fromthe input stream. This step includes finding and marking the state withthe minimum path metric as the ending state. At 930, the method 900 alsoincludes producing L output bits according to it as the previous output.At 940, the decoding method 900 includes checking whether the endingstate equals the selected initial starting state. If yes, at 944, themethod 900 includes taking the previous output as the final output,otherwise go to step 950 where the starting state is set equal to theprevious ending state. At 960, the CD and SD input symbols are rereadfrom the beginning of the L input symbols, and at 970 the SDVA is run orlaunched for the second time. In step 970, the first CD bits areretrieved as the correct output. At 980, the decoding method 900continues with replacing the beginning CD bits of the previous outputwith the correct output from step 970 to get a final output (i.e., thedecoded bit stream). The method 900 ends at 990 (or returns to 920 or926 for processing a next input bit stream).

FIG. 10 illustrates an encoder 1000 configured to provide tail-biting CCencoding which is adopted as a mandatory FEC scheme in OFDMA mode ofIEEE 802.16d/e system as discussed above. As shown, the encoder 1000receives an input bit stream 1002 having a block length L, a set ofmemory elements or shift registers 1004, 1006, 1008, 1010, 1012, and1014, a pair of adder circuits 1016, 1018, and outputs 2 bits 1020 foreach input bit from stream 1002. The tail-biting Convolutional Codesencoder 1000 is the encoder used in the IEEE 802.16d/e standard (but, ofcourse, other tail-biting CC encoder configurations can be used topractice the invention with the decoding method being useful for thisparticular encoder as well as other tail-biting CC encoderarrangements). For the encoder 1000, the coding rate is ½, theconstraint length is 7, and the generator polynomials are as shownbelow:

G₁=171_(OCT) FOR X

G₂=133_(OCT) FOR Y

The encoder 1000 is initialized with the last six input bits of theinput bit stream before encoding.

To verify the proposed decoding algorithm's performance in a practicalsystem, the inventors compared a tail-biting CC decoder configuredaccording the present invention (e.g., to run the decoding method 900 ofFIG. 9) with that of a tail bits CC decoder as defined in IEEE 802.16d/efor an OFDMA system. The simulation system 1100 shown in FIG. 11 hadthree major parts: a transmitter 1110, a channel 1130 with a multipathchannel 1132 and an AWGN 1134, and a receiver 1140 that produce a biterror rate 1160.

At the transmitter side 1110, a binary source generator 1112 randomlyproduces the bit stream for the simulation chain, and these bits arerandomized in module 1114. The transmitter 1110 includes an encoder 1116that is configured either for tail-bits convolutional codes encoding orfor tail-biting convolutional codes encoding depending on whichsimulation is being performed. After encoding, the encoded data ispunctured by module 1118 to match the required coding rates (in Table1). The punctured data is interleaved by module 1120 and then mapped,frequency multiplexed, and OFDM framed (e.g., for modulation by OPSK,16-ZAM, or 64QAM and further interleaving) by modules 1122, 1124, and1126 before the complex-valued data is fed into an OFDM modulator 1128and transmitted to channel 1130.

TABLE 1 Convolutional Codes with puncturing configuration Code Rate 1/22/3 3/4 X 1 10 101 Y 1 11 110 XY X₁Y₁ X₁Y₁Y₂ X₁Y₁Y₂X₃

At the channel 1130, an AWGN channel module 1134 and a multi-pathchannel module 1132 (SUI serial) are concatenated. At the receiver side,on the assumption of perfect channel estimation and synchronization, thereceiver 1140 performs the exact inverse operations as the transmitterand includes an OFDM demodulator 1142, an OFDM deframing module 1144, anequalizer 1146 linked to the multipath channel 1132, a frequencydemultiplexing module 1148, a demapping module 1150, a deinterleavingmodule 1152, a depuncturing module 1154, a channel decoding module 1156adapted to provide the SDVA decoding described herein, and aderandomization module 1158. It should be noted that the system 1100 wasused for simulation purposes, and it is likely that an implementation ina real-world communications system will differ to practice theinvention, i.e., the receiver 1140 may not include the same modulesshown except that typical implementations would include a decodingmodule 1156 configured to provide the SDVA decoding algorithm of theinvention (e.g., see the decoding process 900 of FIG. 9).

The simulation results from operation of the system 1100 are shown inFIGS. 12 and 13. FIG. 12 provides a graph 1200 of the BER performance ofthe system 1100 using tail bits convolutional codes and tail-bitingconvolutional codes under different modulation and coding schemes overSUI-4 channel. In this simulation, the shortest required block sizeswere used. Tail-bits convolutional codes have over 5 dB gains againsttail-biting convolutional codes when BER is below 10⁻⁴. It can beexpected that tail-biting convolutional codes become better along withincreases in the block size. FIG. 13 provides a graph 1300 of thethroughput performance of the system using tail bits convolutional codesand tail-biting convolutional codes under different modulation andcoding schemes over SUI-4 channel. The graph 1300 shows that tail-bitingconvolutional codes have greater throughput than tail-bits convolutionalcodes when E_(b)/N_(o) is high.

FIG. 14 illustrates a block diagram of a receiver or receiving system1400 that may be configured to provide the SDVA decoding describedherein (such as with reference to FIG. 9). As shown, the receivingsystem 1400 includes memory in the form of read-only memory (ROM) 1410and random-access memory (RAM) 1430. A digital signal processor (DSP)1420 is provided for running software modules such as a tail-biting CCdecoding module that utilizes the SDVA algorithm or method. A signalreceiving circuit 1440 is also included in the system 1400 and iscoupled to an antenna 1450 for receiving signals, X(t), e.g., analogradio or wireless signals. The signals, X(t), are converted into digitalsignals (or input bit streams, frames, or blocks), X(n), by the signalreceiving circuit 1440 that are then passed to the DSP 1420 for decoding(as discussed throughout this description). In a preferred embodiment,the digital signals that are received by the system 1400 have beenencoded based on tail-biting convolutional codes (such as those definedby IEEE 802.16d/e or the like). The ROM 1410 in some embodiments storessoftware or applications that are utilized for controlling the operationof the DSP 1420 such as for decoding tail-biting convolutional codesusing the SDVA algorithm of the invention. RAM 1430 may be used tobuffer output frames or other information used during the decoding bythe decoding (and other receiver) software. The DSP 1420 operates todecode the input bit stream, X(n), and provides a final output 1460,e.g., with a final output 730 that includes a correct or correctedoutput 720 as shown in FIG. 7).

Although the invention has been described and illustrated with a certaindegree of particularity, it is understood that the present disclosurehas been made only by way of example, and that numerous changes in thecombination and arrangement of parts can be resorted to by those skilledin the art without departing from the spirit and scope of the invention,as hereinafter claimed.

1. A receiver for use in a wireless digital communication system,comprising: a receiving circuit for receiving a wireless signal andconverting the wireless signal into a digital input bit stream, whereinthe wireless signal is encoder and transmitted over a channel to thereceiver by a transmitter implementing tail-biting convolutional codes;a decoding module stored in memory of the receiver; and a processor forrunning the decoding module to decode the digital input bit streamincluding performing a search depth Viterbi algorithm (SDVA) on thedigital input bit stream at most two times to produce a decoded finaloutput.
 2. The receiver of claim 1, wherein the running of the decodingmodule further comprises: initializing a correction depth based on alength of the digital input bit stream and the channel; selecting afirst starting state from a set of encoding states and initializing ametric value for the first starting state to a value indicting highlikelihood and metric values for other ones of the encoding states to avalue indicting low likelihood; first reading symbols from the digitalinput bit stream; and performing the SDVA on the first read symbols fromthe first starting state to determine path metrics and identify thehighest likelihood path based on the determined path metrics, wherein anending state for the highest likelihood path is identified and outputassociated with the ending state of the highest likelihood path isidentified as previous output.
 3. The method of claim 2, wherein thehighest likelihood path is determined based on the minimum metric. 4.The method of claim 2 wherein the value indicating the highestlikelihood is a low value.
 5. The method of claim 4, wherein the lowvalue is zero.
 6. The method of claim 2, wherein the value indicatinglow likelihood is a high value.
 7. The method of claim 6, wherein thehigh value is the highest value possible.
 8. The receiver of claim 2,wherein the running of the decoding module further comprises: setting asecond starting state equal to the ending state of the highestlikelihood path; second reading a number of symbols from a beginning ofthe previous output, wherein the number is equal to the correctiondepth; performing the SDVA on the second read symbols from the secondstarting state to generate a correct output; and generating the decodedfinal output by replacing a number of the symbols in the previous outputequal to the correction depth with the correct output.
 9. The receiverof claim 1, wherein the SDVA comprises calculating path metrics of anumber of stages equal to a search depth (SD) with the calculatingbeginning from the starting state and terminating at all possible endingstates and wherein the SDVA further comprises identifying a path havingthe highest likelihood metric at a stage of search depth minus one. 10.The receiver of claim 9, wherein the SDVA further comprises calculatingfrom a first stage to a stage of a length of the block (L) minus two andperforming the calculating for a number of iterations equal to L-SD-1 toselect a survival path in a trellis and wherein the SDVA furthercomprises providing outputs of the last SD bits according to the highestlikelihood path.